





'''simple docstring'''




from __future__ import annotations

import queue


class 					SCREAMING_SNAKE_CASE       :


     def __init__(		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Optional[Any]						=			data
          __A		:					Tuple						=			None
          __A		:					str						=			None






def 			_lowerCAmelCase     (    )	->	TreeNode:
     print('\n********Press N to stop entering at any point of time********\n'     )
     __A		:					Tuple						=			input('Enter the value of the root node: '     ).strip().lower()
     __A		:					queue.Queue						=			queue.Queue()
     __A		:					str						=			TreeNode(int(__snake_case     )     )
     q.put(__snake_case     )
     while not q.empty():
          __A		:					Any						=			q.get()
          __A		:					List[Any]						=			f'Enter the left node of {node_found.data}: '
          __A		:					Tuple						=			input(__snake_case     ).strip().lower() or 'n'
          if check == "n":
               return tree_node
          __A		:					Union[str, Any]						=			TreeNode(int(__snake_case     )     )
          __A		:					List[Any]						=			left_node
          q.put(__snake_case     )
          __A		:					Dict						=			f'Enter the right node of {node_found.data}: '
          __A		:					Union[str, Any]						=			input(__snake_case     ).strip().lower() or 'n'
          if check == "n":
               return tree_node
          __A		:					List[Any]						=			TreeNode(int(__snake_case     )     )
          __A		:					int						=			right_node
          q.put(__snake_case     )
     raise






def 			_lowerCAmelCase     (    __snake_case    :					TreeNode     )	->	None:
     if not isinstance(__snake_case					,				__snake_case     ) or not node:
          return
     print(node.data					,				end=','     )
     pre_order(node.left     )
     pre_order(node.right     )






def 			_lowerCAmelCase     (    __snake_case    :					TreeNode     )	->	None:
     if not isinstance(__snake_case					,				__snake_case     ) or not node:
          return
     in_order(node.left     )
     print(node.data					,				end=','     )
     in_order(node.right     )






def 			_lowerCAmelCase     (    __snake_case    :					TreeNode     )	->	None:
     if not isinstance(__snake_case					,				__snake_case     ) or not node:
          return
     post_order(node.left     )
     post_order(node.right     )
     print(node.data					,				end=','     )






def 			_lowerCAmelCase     (    __snake_case    :					TreeNode     )	->	None:
     if not isinstance(__snake_case					,				__snake_case     ) or not node:
          return
     __A		:					queue.Queue						=			queue.Queue()
     q.put(__snake_case     )
     while not q.empty():
          __A		:					Optional[Any]						=			q.get()
          print(node_dequeued.data					,				end=','     )
          if node_dequeued.left:
               q.put(node_dequeued.left     )
          if node_dequeued.right:
               q.put(node_dequeued.right     )






def 			_lowerCAmelCase     (    __snake_case    :					TreeNode     )	->	None:
     if not isinstance(__snake_case					,				__snake_case     ) or not node:
          return
     __A		:					queue.Queue						=			queue.Queue()
     q.put(__snake_case     )
     while not q.empty():
          __A		:					str						=			[]
          while not q.empty():
               __A		:					List[Any]						=			q.get()
               print(node_dequeued.data					,				end=','     )
               if node_dequeued.left:
                    list_.append(node_dequeued.left     )
               if node_dequeued.right:
                    list_.append(node_dequeued.right     )
          print()
          for node in list_:
               q.put(__snake_case     )






def 			_lowerCAmelCase     (    __snake_case    :					TreeNode     )	->	None:
     if not isinstance(__snake_case					,				__snake_case     ) or not node:
          return
     __A		:					list[TreeNode]						=			[]
     __A		:					List[str]						=			node
     while n or stack:
          while n:  # start from root node, find its left child
               print(n.data					,				end=','     )
               stack.append(__snake_case     )
               __A		:					List[str]						=			n.left
          # end of while means current node doesn't have left child
          __A		:					Optional[int]						=			stack.pop()
          # start to traverse its right child
          __A		:					Optional[int]						=			n.right






def 			_lowerCAmelCase     (    __snake_case    :					TreeNode     )	->	None:
     if not isinstance(__snake_case					,				__snake_case     ) or not node:
          return
     __A		:					list[TreeNode]						=			[]
     __A		:					List[Any]						=			node
     while n or stack:
          while n:
               stack.append(__snake_case     )
               __A		:					Tuple						=			n.left
          __A		:					int						=			stack.pop()
          print(n.data					,				end=','     )
          __A		:					Optional[Any]						=			n.right






def 			_lowerCAmelCase     (    __snake_case    :					TreeNode     )	->	None:
     if not isinstance(__snake_case					,				__snake_case     ) or not node:
          return
     __A					,__A		:					int						=			[], []
     __A		:					Any						=			node
     stacka.append(__snake_case     )
     while stacka:  # to find the reversed order of post order, store it in stack2
          __A		:					Optional[int]						=			stacka.pop()
          if n.left:
               stacka.append(n.left     )
          if n.right:
               stacka.append(n.right     )
          stacka.append(__snake_case     )
     while stacka:  # pop up from stack2 will be the post order
          print(stacka.pop().data					,				end=','     )






def 			_lowerCAmelCase     (    __snake_case    :					str = ""					,				__snake_case    :					Dict=50					,				__snake_case    :					Optional[int]="*"     )	->	str:
     if not s:
          return "\n" + width * char
     __A					,__A		:					Optional[Any]						=			divmod(width - len(__snake_case     ) - 2					,				2     )
     return f'{left * char} {s} {(left + extra) * char}'


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(prompt('''Binary Tree Traversals'''))

  lowercase__       : TreeNode         =       build_tree()
  print(prompt('''Pre Order Traversal'''))
  pre_order(node)
  print(prompt() + '''\n''')

  print(prompt('''In Order Traversal'''))
  in_order(node)
  print(prompt() + '''\n''')

  print(prompt('''Post Order Traversal'''))
  post_order(node)
  print(prompt() + '''\n''')

  print(prompt('''Level Order Traversal'''))
  level_order(node)
  print(prompt() + '''\n''')

  print(prompt('''Actual Level Order Traversal'''))
  level_order_actual(node)
  print('''*''' * 50 + '''\n''')

  print(prompt('''Pre Order Traversal - Iteration Version'''))
  pre_order_iter(node)
  print(prompt() + '''\n''')

  print(prompt('''In Order Traversal - Iteration Version'''))
  in_order_iter(node)
  print(prompt() + '''\n''')

  print(prompt('''Post Order Traversal - Iteration Version'''))
  post_order_iter(node)
  print(prompt())




'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class 						lowerCamelCase_				(       snake_case_     ,					unittest.TestCase      ):
						_lowerCAmelCase  : str						  =					KandinskyInpaintPipeline
						_lowerCAmelCase  : List[Any]						  =					['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
						_lowerCAmelCase  : int						  =					[
						    'prompt',
						    'negative_prompt',
						    'image_embeds',
						    'negative_image_embeds',
						    'image',
						    'mask_image',
						]
						_lowerCAmelCase  : List[str]						  =					[
						    'generator',
						    'height',
						    'width',
						    'latents',
						    'guidance_scale',
						    'negative_prompt',
						    'num_inference_steps',
						    'return_dict',
						    'guidance_scale',
						    'num_images_per_prompt',
						    'output_type',
						    'return_dict',
						]
						_lowerCAmelCase  : Union[str, Any]						  =					False







						@property
						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													return 32







						@property
						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													return 32







						@property
						def        __lowercase     (	self :   Optional[int]					):

													"""simple docstring"""
													return self.time_input_dim







						@property
						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													return self.time_input_dim * 4







						@property
						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													return 1_00







						@property
						def        __lowercase     (	self :   Tuple					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''					)
													return tokenizer







						@property
						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													torch.manual_seed(0					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      MCLIPConfig(
													    numDims=self.cross_attention_dim					,     transformerDimensions=self.text_embedder_hidden_size					,     hidden_size=self.text_embedder_hidden_size					,     intermediate_size=37					,     num_attention_heads=4					,     num_hidden_layers=5					,     vocab_size=10_05					,     )

													SCREAMING_SNAKE_CASE       :   Any         =      MultilingualCLIP(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      text_encoder.eval()

													return text_encoder







						@property
						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													torch.manual_seed(0					)

													SCREAMING_SNAKE_CASE       :   Tuple         =      {
													    '''in_channels''': 9,
													    # Out channels is double in channels because predicts mean and variance
													    '''out_channels''': 8,
													    '''addition_embed_type''': '''text_image''',
													    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
													    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
													    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
													    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
													    '''layers_per_block''': 1,
													    '''encoder_hid_dim''': self.text_embedder_hidden_size,
													    '''encoder_hid_dim_type''': '''text_image_proj''',
													    '''cross_attention_dim''': self.cross_attention_dim,
													    '''attention_head_dim''': 4,
													    '''resnet_time_scale_shift''': '''scale_shift''',
													    '''class_embed_type''': None,
													}

													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      UNetaDConditionModel(**lowerCAmelCase__					)
													return model







						@property
						def        __lowercase     (	self :   Optional[Any]					):

													"""simple docstring"""
													return {
													    "block_out_channels": [32, 64],
													    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
													    "in_channels": 3,
													    "latent_channels": 4,
													    "layers_per_block": 1,
													    "norm_num_groups": 8,
													    "norm_type": "spatial",
													    "num_vq_embeddings": 12,
													    "out_channels": 3,
													    "up_block_types": [
													        "AttnUpDecoderBlock2D",
													        "UpDecoderBlock2D",
													    ],
													    "vq_embed_dim": 4,
													}







						@property
						def        __lowercase     (	self :   Union[str, Any]					):

													"""simple docstring"""
													torch.manual_seed(0					)
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      VQModel(**self.dummy_movq_kwargs					)
													return model







						def        __lowercase     (	self :   Optional[int]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Any         =      self.dummy_text_encoder
													SCREAMING_SNAKE_CASE       :   Tuple         =      self.dummy_tokenizer
													SCREAMING_SNAKE_CASE       :   int         =      self.dummy_unet
													SCREAMING_SNAKE_CASE       :   str         =      self.dummy_movq

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      DDIMScheduler(
													    num_train_timesteps=10_00					,     beta_schedule='''linear'''					,     beta_start=0.0_0085					,     beta_end=0.012					,     clip_sample=lowerCAmelCase__					,     set_alpha_to_one=lowerCAmelCase__					,     steps_offset=1					,     prediction_type='''epsilon'''					,     thresholding=lowerCAmelCase__					,     )

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      {
													    '''text_encoder''': text_encoder,
													    '''tokenizer''': tokenizer,
													    '''unet''': unet,
													    '''scheduler''': scheduler,
													    '''movq''': movq,
													}

													return components







						def        __lowercase     (	self :   Dict					,     lowerCAmelCase__ :   Optional[Any]					,     lowerCAmelCase__ :   List[Any]=0					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      floats_tensor((1, self.cross_attention_dim)					,     rng=random.Random(lowerCAmelCase__					)					).to(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      floats_tensor((1, self.cross_attention_dim)					,     rng=random.Random(seed + 1					)					).to(lowerCAmelCase__					)
													# create init_image
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      floats_tensor((1, 3, 64, 64)					,     rng=random.Random(lowerCAmelCase__					)					).to(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      image.cpu().permute(0					,     2					,     3					,     1					)[0]
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      Image.fromarray(np.uinta(lowerCAmelCase__					)					).convert('''RGB'''					).resize((2_56, 2_56)					)
													# create mask
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      np.ones((64, 64)					,     dtype=np.floataa					)
													SCREAMING_SNAKE_CASE       :   str         =      0

													if str(lowerCAmelCase__					).startswith('''mps'''					):
																				SCREAMING_SNAKE_CASE       :   Tuple         =      torch.manual_seed(lowerCAmelCase__					)
													else:
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      torch.Generator(device=lowerCAmelCase__					).manual_seed(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      {
													    '''prompt''': '''horse''',
													    '''image''': init_image,
													    '''mask_image''': mask,
													    '''image_embeds''': image_embeds,
													    '''negative_image_embeds''': negative_image_embeds,
													    '''generator''': generator,
													    '''height''': 64,
													    '''width''': 64,
													    '''num_inference_steps''': 2,
													    '''guidance_scale''': 4.0,
													    '''output_type''': '''np''',
													}
													return inputs







						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Any         =      '''cpu'''

													SCREAMING_SNAKE_CASE       :   Dict         =      self.get_dummy_components()

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      self.pipeline_class(**lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   int         =      pipe.to(lowerCAmelCase__					)

													pipe.set_progress_bar_config(disable=lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   str         =      pipe(**self.get_dummy_inputs(lowerCAmelCase__					)					)
													SCREAMING_SNAKE_CASE       :   int         =      output.images

													SCREAMING_SNAKE_CASE       :   int         =      pipe(
													    **self.get_dummy_inputs(lowerCAmelCase__					)					,     return_dict=lowerCAmelCase__					,     )[0]

													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      image[0, -3:, -3:, -1]
													SCREAMING_SNAKE_CASE       :   Dict         =      image_from_tuple[0, -3:, -3:, -1]

													print(F"""image.shape {image.shape}"""					)

													assert image.shape == (1, 64, 64, 3)

													SCREAMING_SNAKE_CASE       :   List[str]         =      np.array(
													    [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]					)

													assert (
													    np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
													), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
													assert (
													    np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2
													), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""







						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													super().test_inference_batch_single_identical(expected_max_diff=3e-3					)


@slow
@require_torch_gpu
class 						lowerCamelCase_				(       unittest.TestCase      ):







						def        __lowercase     (	self :   List[str]					):

													"""simple docstring"""
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()







						def        __lowercase     (	self :   Dict					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[Any]         =      load_numpy(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
													    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''					)

													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''					)
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      np.ones((7_68, 7_68)					,     dtype=np.floataa					)
													SCREAMING_SNAKE_CASE       :   List[str]         =      0

													SCREAMING_SNAKE_CASE       :   Dict         =      '''a hat'''

													SCREAMING_SNAKE_CASE       :   str         =      KandinskyPriorPipeline.from_pretrained(
													    '''kandinsky-community/kandinsky-2-1-prior'''					,     torch_dtype=torch.floataa					)
													pipe_prior.to(lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      KandinskyInpaintPipeline.from_pretrained(
													    '''kandinsky-community/kandinsky-2-1-inpaint'''					,     torch_dtype=torch.floataa					)
													SCREAMING_SNAKE_CASE       :   Dict         =      pipeline.to(lowerCAmelCase__					)
													pipeline.set_progress_bar_config(disable=lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      torch.Generator(device='''cpu'''					).manual_seed(0					)
													SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Any         =      pipe_prior(
													    lowerCAmelCase__					,     generator=lowerCAmelCase__					,     num_inference_steps=5					,     negative_prompt=''''''					,     ).to_tuple()

													SCREAMING_SNAKE_CASE       :   Tuple         =      pipeline(
													    lowerCAmelCase__					,     image=lowerCAmelCase__					,     mask_image=lowerCAmelCase__					,     image_embeds=lowerCAmelCase__					,     negative_image_embeds=lowerCAmelCase__					,     generator=lowerCAmelCase__					,     num_inference_steps=1_00					,     height=7_68					,     width=7_68					,     output_type='''np'''					,     )

													SCREAMING_SNAKE_CASE       :   Tuple         =      output.images[0]

													assert image.shape == (7_68, 7_68, 3)

													assert_mean_pixel_difference(lowerCAmelCase__					,     lowerCAmelCase__					)




